Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

This work describes an experimental study of the flow field and wall pressure fluctuations induced by quasi-two-dimensional incompressible turbulent boundary layers overflowing a forward-facing step (FFS). Pressure fluctuations are measured upstream and downstream of an instrumented FFS step model installed inside a large scale recirculation water tunnel, while two-dimensional (2D) velocity fields are measured close to the step via 2D particle image velocimetry (PIV). The overall flow physics is studied in terms of averaged velocity and vorticity fields for different Reynolds numbers based on the step’s height. The wall pressure statistics are analyzed in terms of several indicators, including the root mean squares and probability density functions of the pressure fluctuations, demonstrating that the most relevant flow structure is the unsteady recirculation bubble formed at the reattachment region downstream of the step. Pressure spectra and cross correlations are computed as well, and the convection velocity characterizing the propagation of hydrodynamic perturbations is determined as a function of the distance to the vertical side of the step. The simultaneous measurement of time-resolved PIV fields and wall pressure signals enabled us to compute the pressure/velocity cross correlations in the region downstream of the step and substantiated the relevant role played by the recirculation bubble.

A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weak-scaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance. Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. codes present particular challenges to computational performance. We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. codes present particular challenges to computational performance. We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

The present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone.